4 Questions to Ask Before You Build Your Own Power Market Data Infrastructure

You've got a trader, an analyst, or an engineer on your team who's spent an afternoon with Claude or Copilot and come back with a working prototype: a data collector, a dashboard, a pipeline that pulls prices straight from an ISO. It works. It's fast. And it raises an obvious question: Why keep paying a vendor for something your own team just built in a weekend?

It's a fair question. AI tools really have made it faster than ever to start building. What they haven't changed is what it costs to keep that system running three years from now, when the ISO changes a report format without warning, a revision pattern breaks your historical mapping, or a schema update quietly corrupts the data feeding a live trading decision.

The bigger question is not whether you can build something, but whether you should, and what it actually costs to maintain it once it's live. Before your team commits its time to a build, here are four questions worth asking.

Question 1: Is This Core to Your Competitive Differentiation?

The raw inputs to most power market analysis (e.g., ISO price, generation, load, transmission, and outage data) are publicly available. Every market participant has access to the same underlying sources. Competitive advantage does not come from having this data. It comes from doing something with it that is faster, more accurate, or more insightful than what competitors do.

What is not public is what Yes Energy does to that data: the cleaning, normalization, enrichment, business context creation, revision management, and vintage integrity that transform raw ISO feeds into AI-ready information. Proprietary datasets like Live Power provide grid visibility unavailable from any public source. And as Yes Energy continues to bring public data, proprietary data, and customer-specific data together across historical, real-time, forecast, and modeling dimensions, the gap between what a specialist can provide and what any organization can build internally keeps widening.

For foundational data and software infrastructure, building internally does more than just fail to create a competitive advantage. It actively creates a disadvantage. Every month spent building and maintaining infrastructure that a specialist already does better is a month competitors who bought that foundation are spending on the models, signals, and strategies that actually move P&L. That gap compounds rather than closes.

Question 2: Do You Have the Domain Expertise to Maintain It at Production Quality?

Not to build it. To maintain it through every ISO schema change, every market transition, every revision edge case, and every unexpected failure for as long as your organization operates.

If the honest answer is “we would figure it out,” that is a risk assessment, not a plan. The volume of data reporting issues that specialized operations teams handle annually reflects not incompetence among organizations that try to build internally, but rather the sheer operational complexity of this domain. The issues do not stop occurring because the internal team is skilled. They stop causing problems because a specialized organization has built the processes, alerting systems, and institutional knowledge to catch and resolve them before they matter.

Question 3: What Is the Opportunity Cost of the Capacity You'd Redirect?

The most underweighted factor in any build vs. buy analysis is opportunity cost. The question is not just “what does it cost to build this?” It is “what does it cost us to not build something else with those same people?”

A data engineering team that spends nearly half its time maintaining a data pipeline effectively has half a team available for higher-value work. Over three years, at loaded compensation of $160,000 to $180,000 per person, that is hundreds of thousands of dollars in analytical and modeling capacity never deployed against the problems that drive competitive advantage.

Question 4: What Is the Cost of Being Wrong?

In a live trading environment, the risk of an incorrect build isn't symmetric. A vendor who fails to deliver can be replaced. An internal system that fails during a live session, or that produces subtly incorrect data that goes undetected for weeks, creates losses that can't be recovered.

Reza Haidari of LSEG framed the buy decision this way: "Initially, we did attempt to collect the ISO data ourselves. I concluded that because of the changing needs of our customers, the dynamically evolving power markets and complex data sets within the power markets, we would be better served by partnering with a company that specializes in ISO source data… Both we and our customers benefit from their reliability, accuracy, timeliness, and ongoing maintenance of ISO pricing data."

That's a judgment about risk-adjusted decision-making, not about capability, and it's exactly the analytical framework power market participants apply to every other consequential decision they make.

Where This Leaves You

None of this is an argument against AI. It's an argument for pointing it at the right layer. The teams that win aren't the ones using AI to rebuild their data infrastructure from scratch. They're the ones using it to build better trading strategies, sharper forecasts, and stronger risk models on top of data they already trust.

These four questions won't settle every build vs. buy decision on their own, but they'll tell you whether you're looking at a genuine opportunity to differentiate or a costly distraction from the work that actually moves the needle.

Want the full picture, including the real total cost of ownership behind a build and where AI creates the most value once your data foundation is solid? Read the full white paper, The Build vs. Buy Decision in the AI Era.